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Genome Biology Papers on Placental Methylome in Autism Spectrum Disorder, viralFlye, StrainGE

A University of California, Davis-led team presents an autism spectrum disorder association study focused on DNA methylation patterns in placental tissue. With bisulfite sequencing data from 204 placental samples from women with or without ASD-affected children, the investigators identified more than 130 differentially methylated regions in placental samples linked to ASD cases. Within that set, they focused in on a chromosome 22 locus near the NHIP gene, which showed lower-than-usual DNA methylation in ASD-associated placental samples, along with reduced NHIP gene expression in subsequent analyses of placental and post-mortem ASD brain samples. "Together, these results identify and initially characterize a novel environmentally responsive ASD risk gene relevant to brain development in a hitherto under-characterized region of the human genome," they write.

Investigators at Saint Petersburg State University and the University of California, San Diego, introduce a tool called viralFlye for assembling and analyzing viral genomes from long-read metagenomic sequence data, including viral host-related analyses. When the team applied viralFlye to long-read metagenome assemblies, for example, it unearthed previously unappreciated CRISPR arrays. "We show that our viralFlye approach recovers up to 2.25 times more complete or nearly complete viral sequences, as compared to the previously published pipelines, while reducing the number of misassemblies," the authors report. "We also show that long reads improve the accuracy of virus-host association predictions … based on matching of the CRISPR-Cas sites."

Finally, researchers at the Broad Institute and elsewhere outline a toolkit for teasing out microbial strain profiles — including genetic data on strains with potential clinical importance — from low-coverage short-read metagenomic sequence data generated from microbial community mixtures. The "Strain Genome Explorer" (StrainGE) method "deconvolutes strain mixtures and characterizes component strains at the nucleotide level from short-read metagenomic sequencing with higher sensitivity and resolution than other tools," the team writes. After applying the approach to synthetic sequence data, and setting it against other strain detection methods, the authors used StrainGE to analyze metagenomic sequence data from human gut samples, uncovering clinically relevant strains of bugs such as Escherichia coli and Enterococcus.

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